With the growth of connected infrastructure, more and more human interactions are happening online through instant messaging, real time interactions on online social communities, or interactions facilitated with next general mobile and connected devices that include smart phones, internet tablets, gaming consoles, and more traditional laptops and computer terminals. One key aspect of these interactions is the ability to accurately convey an individual's emotions during such online interactions.
Currently such emotions are being conveyed by individuals in a deliberate manner by text or other visual cues. There even exist methods for automatically detecting individual emotions based on a variety of sensory, auditory and visual inputs.
However, the currently known technologies do not provide a solution that addresses a uniform method of conveying an individual's emotions in a connected environment that can be scaled across a number of online social interactions.
Furthermore, there doesn't exist a system or method that could capture, normalize and share an instant reaction of an individual (“Emotional State”) as a result of viewing or consuming content that is shared among a large number of users.
In light of above, a method and a system is presented that can detect instant and time averaged “Emotional Scores” or “Emotional Profiles” of individuals who are viewing or watching content that is shared in a connected environment. Furthermore, the method and the system can be used to create a personalized emotional profile of an individual, using a subset of this personalized emotional profile to represent the individual's behavior during a specific application, and to derive prediction and analytics by combining this with the individual's instantaneous behavior in the given application.